Browse published AI skills maintained by doanchienthangdev. Compare skill descriptions and follow each source repository for setup instructions.
Indexed skills
128Repository stars across indexed skills
384Repository forks
128Average repository stars per indexed skill
3Repository stars may repeat across skills from the same repository.
Processes images with Sharp for optimization, resizing, format conversion, and batch operations. Use when optimizing web images, generating thumbnails, creating responsive image sets, or applying transformation...
Claude writes reliable E2E tests using Playwright for browser automation. Use when writing end-to-end tests, implementing Page Object Model, visual regression testing, API mocking, or cross-browser testing.
AI agent maximizes efficiency and minimizes costs through strategic token usage while maintaining output quality. Use when managing AI interactions, designing prompts, or reducing costs.
Claude designs scalable React component architectures using compound components, custom hooks, and state machines. Use when building reusable UI systems or complex component APIs.
Designs scalable, resilient architectures using the 7 Omega principles. Use when building systems that need to scale to millions of users or designing platform-level infrastructure.
AI application architecture - gateway, orchestration, model routing, observability, deployment patterns. Use when designing AI systems, scaling applications, or building production infrastructure.
Advanced security patterns including zero-trust architecture, secret management, CSP, and compliance frameworks
Builds production React applications with hooks, TypeScript, state management, and performance optimization. Use when creating React SPAs, component systems, or interactive UIs.
AI agent designs layered security architecture with multiple independent protective barriers ensuring no single point of failure. Use when building security systems, reviewing architecture, or hardening applica...
ML serving optimization techniques including batching, caching, model compilation, and latency reduction for production ML systems.
Builds production Express.js APIs with TypeScript, middleware patterns, authentication, and error handling. Use when creating Node.js backends, REST APIs, or Express applications.
Next.js development with App Router, Server Components, API routes, and full-stack patterns
Performance testing patterns including load testing, stress testing, benchmarking, and profiling for optimal application performance
Building and processing datasets - data quality, curation, deduplication, synthesis, annotation, formatting. Use when creating training data, improving data quality, or generating synthetic data.
Collecting and using user feedback - explicit/implicit signals, feedback analysis, improvement loops, A/B testing. Use when improving AI systems, understanding user satisfaction, or iterating on quality.
The agent implements chaos engineering practices for building resilient systems. Use when testing fault tolerance, designing game days, or validating system recovery.
ML development workflow covering experiment design, baseline establishment, iterative improvement, and experiment tracking best practices.
AI agent requests effective code reviews through proper preparation, clear descriptions, and appropriate reviewer selection. Use when creating PRs, requesting feedback, or submitting for review.
AI agent validates work through evidence-based verification with comprehensive checklists, automated validation, and proof-of-work documentation. Use when completing tasks, preparing for review, or ensuring qua...
Building production AI applications with Foundation Models. Covers prompt engineering, RAG, agents, finetuning, evaluation, and deployment. Use when working with LLMs, building AI features, or architecting AI s...
Optimizing AI inference - quantization, speculative decoding, KV cache, batching, caching strategies. Use when reducing latency, lowering costs, or scaling AI serving.
ML deployment paradigms including batch vs real-time inference, online vs offline serving, edge deployment, and serverless ML.
WebSocket, Server-Sent Events, and real-time communication patterns for live features
Implements comprehensive testing across all quality dimensions - accuracy, performance, security, and accessibility. Use when building test strategies or ensuring production-grade quality assurance.
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.